

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def   __UpperCAmelCase					(      __a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[Any]   =						os.path.join(args.tf_model_dir						,'''parameters.json'''      )
					_a      :       Tuple   =						json.loads(open(__a      ).read()      )
					if not params:
										raise ValueError(
										    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""      )
					if not args.output.endswith('''.pt'''      ):
										_a      :       str   =						args.output + '''.pt'''
					_a      :       List[str]   =						OrderedDict()
					with tf.device('''/CPU:0'''      ):
										_a      :       Optional[Any]   =						tf.train.load_checkpoint(args.tf_model_dir      )
										_a      :       str   =						reader.get_variable_to_shape_map()
										for key_name in shapes.keys():
															_a      :       Any   =						reader.get_tensor(__a      ).astype(np.floataa      )
															if key_name.endswith('''/adam_m'''      ) or key_name.endswith('''/adam_v'''      ):
																				continue
															if key_name.startswith('''pasts/'''      ):
																				if key_name.startswith('''pasts/mlp'''      ):
																									_a      :       str   =						int(key_name[9]      )
																				elif key_name.startswith('''pasts/out'''      ):
																									_a      :       List[Any]   =						8
																				_a      :       str   =						'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																				_a      :       Optional[Any]   =						vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_a      :       Any   =						torch.tensor(__a      )
															elif key_name.startswith('''model/moe'''      ):
																				_a      :       Dict   =						int(key_name[9:].split('''/'''      )[0]      )
																				if key_name.endswith('''/switch_gating/kernel'''      ):
																									_a      :       str   =						'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
																									_a      :       Dict   =						vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       Optional[int]   =						torch.tensor(__a      )
																				elif key_name.endswith('''/softmlp/kernel'''      ):
																									_a      :       int   =						'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
																									_a      :       List[Any]   =						vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       Dict   =						torch.tensor(__a      )
																				elif key_name.endswith('''/wo/kernel'''      ) or key_name.endswith('''/wi/kernel'''      ):
																									_a      :       Union[str, Any]   =						key_name[-9:-7]
																									for i in range(16      ):
																														_a      :       Dict   =						'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																														_a      :       Any   =						(
																														    vnp[i].transpose([1, 0]      ).copy()
																														)  # In Mesh-Tensorflow, it is one array, so it is divided
																														_a      :       List[Any]   =						torch.tensor(__a      )
															elif key_name.startswith('''model/mlp'''      ):
																				_a      :       Any   =						int(key_name[9:].split('''/'''      )[0]      )
																				if key_name.endswith('''/p1/kernel'''      ):
																									_a      :       str   =						'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
																									_a      :       Dict   =						vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       Any   =						torch.tensor(__a      )
																				elif key_name.endswith('''/p1/bias'''      ):
																									_a      :       Any   =						'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
																									_a      :       int   =						vnp.copy()  # same because it is one dimensional
																									_a      :       Any   =						torch.tensor(__a      )
																				elif key_name.endswith('''/p2/kernel'''      ):
																									_a      :       Tuple   =						'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
																									_a      :       List[str]   =						vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       Union[str, Any]   =						torch.tensor(__a      )
																				elif key_name.endswith('''/p2/bias'''      ):
																									_a      :       List[Any]   =						'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
																									_a      :       List[str]   =						vnp.copy()  # same because it is one dimensional
																									_a      :       Dict   =						torch.tensor(__a      )
															elif key_name.startswith('''model/ln'''      ):
																				_a      :       Optional[int]   =						int(key_name[8:].split('''/'''      )[0]      )
																				if key_name.endswith('''/b'''      ):
																									_a      :       List[str]   =						'''model.blocks.%d.feed_forward.norm.bias''' % player
																									_a      :       Union[str, Any]   =						vnp.copy()  # same because it is one dimensional
																									_a      :       str   =						torch.tensor(__a      )
																				elif key_name.endswith('''/g'''      ):
																									_a      :       List[Any]   =						'''model.blocks.%d.feed_forward.norm.weight''' % player
																									_a      :       int   =						vnp.copy()  # same because it is one dimensional
																									_a      :       List[str]   =						torch.tensor(__a      )
															elif key_name.startswith('''model/att'''      ):
																				_a      :       List[str]   =						int(key_name[9:].split('''/'''      )[0]      )
																				if key_name.endswith('''/qkv/kernel'''      ):
																									_a      :       List[str]   =						vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																									_a      :       int   =						state[:, 0, :, :]
																									_a      :       int   =						state[:, 1, :, :]
																									_a      :       Optional[Any]   =						state[:, 2, :, :]
																									_a      :       Tuple   =						(
																									    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
																									    .transpose([1, 0]      )
																									    .copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       Union[str, Any]   =						(
																									    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
																									    .transpose([1, 0]      )
																									    .copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       str   =						(
																									    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
																									    .transpose([1, 0]      )
																									    .copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       str   =						'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
																									_a      :       Optional[Any]   =						torch.tensor(__a      )
																									_a      :       Tuple   =						'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																									_a      :       int   =						torch.tensor(__a      )
																									_a      :       Optional[Any]   =						'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																									_a      :       Any   =						torch.tensor(__a      )
																				elif key_name.endswith('''/o/kernel'''      ):
																									_a      :       Dict   =						'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
																									_a      :       Union[str, Any]   =						(
																									    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																									)  # Mesh-Tensorflow is a diagonal matrix
																									_a      :       List[Any]   =						torch.tensor(__a      )
															elif key_name.startswith('''model/an'''      ):
																				_a      :       Union[str, Any]   =						int(key_name[8:].split('''/'''      )[0]      )
																				if key_name.endswith('''/b'''      ):
																									_a      :       List[Any]   =						'''model.blocks.%d.self_attn.norm.bias''' % player
																									_a      :       Any   =						vnp.copy()  # same because it is one dimensional
																									_a      :       Dict   =						torch.tensor(__a      )
																				elif key_name.endswith('''/g'''      ):
																									_a      :       List[Any]   =						'''model.blocks.%d.self_attn.norm.weight''' % player
																									_a      :       Optional[Any]   =						vnp.copy()  # same because it is one dimensional
																									_a      :       List[Any]   =						torch.tensor(__a      )
															elif (
															    key_name.startswith('''model/wte'''      )
															    or key_name.startswith('''model/wpe'''      )
															    or key_name.startswith('''model/ete'''      )
															):
																				_a      :       Optional[Any]   =						{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
																				    key_name[-3:]
																				]
																				_a      :       Tuple   =						'''model.%s.weight''' % nlayer
																				_a      :       Dict   =						vnp.copy()  # same in embedded
																				_a      :       Optional[int]   =						torch.tensor(__a      )
																				if key_name.startswith('''model/wte'''      ):
																									_a      :       Optional[int]   =						'''lm_head.weight'''
																									_a      :       Union[str, Any]   =						vnp.copy()  # same in embedded
																									_a      :       Union[str, Any]   =						torch.tensor(__a      )
															elif key_name.startswith('''model/wob'''      ):
																				_a      :       Union[str, Any]   =						'''final_logits_bias'''
																				_a      :       str   =						vnp.copy()  # same in embedded
																				_a      :       str   =						state.reshape((1, -1)      )
																				_a      :       Any   =						torch.tensor(__a      )
															elif key_name == "model/dense/kernel":
																				_a      :       List[str]   =						'''model.last_project.weight'''
																				_a      :       Optional[Any]   =						vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_a      :       Union[str, Any]   =						torch.tensor(__a      )
															elif key_name == "model/dense_1/bias":
																				_a      :       Optional[int]   =						'''model.last_project.bias'''
																				_a      :       Dict   =						vnp.copy()  # same because it is one dimensional
																				_a      :       Tuple   =						torch.tensor(__a      )
					torch.save(__a						,args.output      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			a__												=						parser.parse_args()
			convert_tf_gptsan_to_pt(args)








'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




